


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""ClapFeatureExtractor"""
							_UpperCAmelCase      =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					kwargs.pop('sampling_rate'			,						lowerCAmelCase__       )

										if text is None and audios is None:
													raise ValueError('You have to specify either text or audios. Both cannot be none.'       )

										if text is not None:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if audios is not None:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.feature_extractor(
													    lowerCAmelCase__			,						sampling_rate=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and audios is not None:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					audio_features.input_features
													return encoding
										elif text is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**lowerCAmelCase__       )			,						tensor_type=lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names       )       )






"""simple docstring"""


import os
from distutils.util import strtobool





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						for e in env_keys:
												lowerCamelCase__   =int(os.environ.get(__lowerCAmelCase  ,       -1							)							)
												if val >= 0:
																		return val
						return default





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase=False							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =os.environ.get(__lowerCAmelCase  ,       str(__lowerCAmelCase							)							)
						return strtobool(__lowerCAmelCase							) == 1  # As its name indicates `strtobool` actually returns an int...





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase="no"							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   =os.environ.get(__lowerCAmelCase  ,       str(__lowerCAmelCase							)							)
						return value


